We have developed PRIDE (Prediction In Dynamic Environments), a hierarchical multi-resolutional framework
for moving object prediction that incorporates multiple prediction algorithms into a single, unifying framework.
PRIDE incorporates two approaches for the prediction of the future location of moving objects at various levels
of resolution at the frequency and level of abstraction necessary for planners at different levels within the
hierarchy. These approaches, termed long-term (LT) and short-term (ST) predictions, respectively, are based
on situation recognition and vehicle models for moving object prediction using sensor data. Our recent efforts
have demonstrated the ability to use the results of the short-term prediction algorithms to strengthen/weaken
the estimates of the long-term prediction algorithms. Based on previous experiments, we have found that the
short-term prediction algorithms perform best when predicting on the order of a few seconds into the future
and that the longer-term prediction algorithms are best at predicting on the order of several seconds into the
future. In this paper, we explore the time window in which both the short-term and the long-term prediction
algorithms provide reasonable results. Additionally, we describe a methodology by which we can determine
the time point at which the short-term prediction algorithm no longer provides results within an acceptable
predefined error threshold. We provide experimental results in an autonomous on-road driving scenario using
AutoSim, a high-fidelity simulation tool that models details about road networks, including individual lanes,
lane markings, intersections, legal intersection traversability, etc.